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• W4289704803 abstract "The advent of Internet-based technology has made daily life much easy than earlier days. The exponential rise in the popularity of social media platforms has not only connected people from faraway places, but has also increased communication among humans. However, in several instances, social media platforms have also been utilized for unethical and criminal activities. The propagation of fake news on social media during the ongoing COVID-19 pandemic has deteriorated the mental and physical health of people. Therefore, to control the flow of fake news regarding the novel coronavirus, several studies have been undertaken to automatically detect the fake news about COVID-19 using various intelligent techniques. However, different studies have shown different results on the performance of the predicting models. In this paper, we have evaluated several machine learning and deep learning models for the automatic detection of fake news regarding COVID-19. The experiments were carried out on two publicly available datasets, and the results were assessed using several evaluation metrics. The traditional machine learning models produced better results than the deep learning models in predicting fake news." @default.
• W4289704803 created "2022-08-04" @default.
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• W4289704803 date "2022-08-03" @default.
• W4289704803 modified "2023-10-14" @default.
• W4289704803 title "Evaluating Intelligent Methods for Detecting COVID-19 Fake News on Social Media Platforms" @default.
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• W4289704803 doi "https://doi.org/10.3390/electronics11152417" @default.
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